PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning from Labeled and Unlabeled Data on a Directed Graph
Dengyong Zhou, Jiayuan Huang and Bernhard Schölkopf
In: ICML 2005, 7-11 August 2005, Bonn, Germany.

Abstract

We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1028
Deposited By:Dengyong Zhou
Deposited On:22 July 2005